PROPOSAL FOR HANDLING MISSING DATA
PROPOSAL FOR HANDLING MISSING DATA
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DOI：
10.1007/bf02291569
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发表时间：
1975-01-01
期刊：
影响因子：
3
通讯作者：
STAELIN, R
中科院分区：
文献类型：
--
作者：
GLEASON, TC;STAELIN, R
提出了一种处理多元数据中缺失观测值问题的方法，并对该方法进行了评价。该方法使用数据的主成分的变换来估计缺失条目。通过对42个计算机生成的数据矩阵的Monte Carlo研究，研究了该方法和四种替代方法的性质。比较了这两种方法预测相关矩阵和缺失项的能力，结果表明，只要变量之间存在适度的相关性（即，平均非对角相关性高于0.2）所提出的方法至少与最佳替代方案（回归方法）一样好，同时在计算上相当快且更简单。给出了基于矩阵的易于计算的特性来确定最佳替代方案的模型。这些模型的一般性证明使用以前发表的结果Timm。
A method for dealing with the problem of missing observations in multivariate data is developed and evaluated. The method uses a transformation of the principal components of the data to estimate missing entries. The properties of this method and four alternative methods are investigated by means of a Monte Carlo study of 42 computer-generated data matrices. The methods are compared with respect to their ability to predict correlation matrices as well as missing entries.The results indicate that whenever there exists modest intercorrelations among the variables (i.e., average off diagonal correlation above .2) the proposed method is at least as good as the best alternative (a regression method) while being considerably faster and simpler computationally. Models for determining the best alternative based upon easily calculated characteristics of the matrix are given. The generality of these models is demonstrated using the previously published results of Timm.